Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

With the extensive application of deep learning (DL) algorithms in recent years, eg, for
detecting Android malware or vulnerable source code, artificial intelligence (AI) and …

Abstract Visual Counterfactual Explanations (VCEs) are an important tool to understand the
decisions of an image classifier. They are “small” but “realistic” semantic changes of the …

Deep neural networks (DNNs) have greatly advanced the ability to predict genome function
from sequence. However, elucidating underlying biological mechanisms from genomic …

Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has
become an important aspect of research in the field of deep learning due to their …

Explanation methods shed light on the decision process of black-box classifiers such as
deep neural networks. But their usefulness can be compromised because they are …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

Counterfactual examples are one of the most commonly-cited methods for explaining the
predictions of machine learning models in key areas such as finance and medical diagnosis …

To explain predictions made by complex machine learning models, many feature attribution
methods have been developed that assign importance scores to input features. Some recent …

Visual counterfactual explanations (VCEs) in image space are an important tool to
understand decisions of image classifiers as they show under which changes of the image …